/*
 *   This program is free software: you can redistribute it and/or modify
 *   it under the terms of the GNU General Public License as published by
 *   the Free Software Foundation, either version 3 of the License, or
 *   (at your option) any later version.
 *
 *   This program is distributed in the hope that it will be useful,
 *   but WITHOUT ANY WARRANTY; without even the implied warranty of
 *   MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 *   GNU General Public License for more details.
 *
 *   You should have received a copy of the GNU General Public License
 *   along with this program.  If not, see <http://www.gnu.org/licenses/>.
 */

/*
 *    FarthestFirst.java
 *    Copyright (C) 2002-2012 University of Waikato, Hamilton, New Zealand
 *
 */
package weka.clusterers;

import java.util.Collections;
import java.util.Enumeration;
import java.util.Random;
import java.util.Vector;

import weka.core.Attribute;
import weka.core.Capabilities;
import weka.core.Capabilities.Capability;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.Option;
import weka.core.TechnicalInformation;
import weka.core.TechnicalInformation.Field;
import weka.core.TechnicalInformation.Type;
import weka.core.TechnicalInformationHandler;
import weka.core.Utils;
import weka.filters.Filter;
import weka.filters.unsupervised.attribute.ReplaceMissingValues;

/**
 * <!-- globalinfo-start --> Cluster data using the FarthestFirst
 * algorithm.<br/>
 * <br/>
 * For more information see:<br/>
 * <br/>
 * Hochbaum, Shmoys (1985). A best possible heuristic for the k-center problem.
 * Mathematics of Operations Research. 10(2):180-184.<br/>
 * <br/>
 * Sanjoy Dasgupta: Performance Guarantees for Hierarchical Clustering. In: 15th
 * Annual Conference on Computational Learning Theory, 351-363, 2002.<br/>
 * <br/>
 * Notes:<br/>
 * - works as a fast simple approximate clusterer<br/>
 * - modelled after SimpleKMeans, might be a useful initializer for it
 * <p/>
 * <!-- globalinfo-end -->
 * 
 * <!-- technical-bibtex-start --> BibTeX:
 * 
 * <pre>
 * &#64;article{Hochbaum1985,
 *    author = {Hochbaum and Shmoys},
 *    journal = {Mathematics of Operations Research},
 *    number = {2},
 *    pages = {180-184},
 *    title = {A best possible heuristic for the k-center problem},
 *    volume = {10},
 *    year = {1985}
 * }
 * 
 * &#64;inproceedings{Dasgupta2002,
 *    author = {Sanjoy Dasgupta},
 *    booktitle = {15th Annual Conference on Computational Learning Theory},
 *    pages = {351-363},
 *    publisher = {Springer},
 *    title = {Performance Guarantees for Hierarchical Clustering},
 *    year = {2002}
 * }
 * </pre>
 * <p/>
 * <!-- technical-bibtex-end -->
 * 
 * <!-- options-start --> Valid options are:
 * <p/>
 * 
 * <pre>
 *  -N &lt;num&gt;
 *  number of clusters. (default = 2).
 * </pre>
 * 
 * <pre>
 *  -S &lt;num&gt;
 *  Random number seed.
 *  (default 1)
 * </pre>
 * 
 * <!-- options-end -->
 * 
 * @author Bernhard Pfahringer (bernhard@cs.waikato.ac.nz)
 * @version $Revision$
 * @see RandomizableClusterer
 */
public class FarthestFirst extends RandomizableClusterer implements TechnicalInformationHandler {

    // Todo: rewrite to be fully incremental
    // cleanup, like deleting m_instances

    /** for serialization */
    static final long serialVersionUID = 7499838100631329509L;

    /**
     * training instances, not necessary to keep, could be replaced by
     * m_ClusterCentroids where needed for header info
     */
    protected Instances m_instances;

    /**
     * replace missing values in training instances
     */
    protected ReplaceMissingValues m_ReplaceMissingFilter;

    /**
     * number of clusters to generate
     */
    protected int m_NumClusters = 2;

    /**
     * holds the cluster centroids
     */
    protected Instances m_ClusterCentroids;

    /**
     * attribute min values
     */
    private double[] m_Min;

    /**
     * attribute max values
     */
    private double[] m_Max;

    /**
     * Returns a string describing this clusterer
     * 
     * @return a description of the evaluator suitable for displaying in the
     *         explorer/experimenter gui
     */
    public String globalInfo() {
        return "Cluster data using the FarthestFirst algorithm.\n\n" + "For more information see:\n\n" + getTechnicalInformation().toString() + "\n\n" + "Notes:\n" + "- works as a fast simple approximate clusterer\n" + "- modelled after SimpleKMeans, might be a useful initializer for it";
    }

    /**
     * Returns an instance of a TechnicalInformation object, containing detailed
     * information about the technical background of this class, e.g., paper
     * reference or book this class is based on.
     * 
     * @return the technical information about this class
     */
    @Override
    public TechnicalInformation getTechnicalInformation() {
        TechnicalInformation result;
        TechnicalInformation additional;

        result = new TechnicalInformation(Type.ARTICLE);
        result.setValue(Field.AUTHOR, "Hochbaum and Shmoys");
        result.setValue(Field.YEAR, "1985");
        result.setValue(Field.TITLE, "A best possible heuristic for the k-center problem");
        result.setValue(Field.JOURNAL, "Mathematics of Operations Research");
        result.setValue(Field.VOLUME, "10");
        result.setValue(Field.NUMBER, "2");
        result.setValue(Field.PAGES, "180-184");

        additional = result.add(Type.INPROCEEDINGS);
        additional.setValue(Field.AUTHOR, "Sanjoy Dasgupta");
        additional.setValue(Field.TITLE, "Performance Guarantees for Hierarchical Clustering");
        additional.setValue(Field.BOOKTITLE, "15th Annual Conference on Computational Learning Theory");
        additional.setValue(Field.YEAR, "2002");
        additional.setValue(Field.PAGES, "351-363");
        additional.setValue(Field.PUBLISHER, "Springer");

        return result;
    }

    /**
     * Returns default capabilities of the clusterer.
     * 
     * @return the capabilities of this clusterer
     */
    @Override
    public Capabilities getCapabilities() {
        Capabilities result = super.getCapabilities();
        result.disableAll();
        result.enable(Capability.NO_CLASS);

        // attributes
        result.enable(Capability.NOMINAL_ATTRIBUTES);
        result.enable(Capability.NUMERIC_ATTRIBUTES);
        result.enable(Capability.DATE_ATTRIBUTES);
        result.enable(Capability.MISSING_VALUES);

        return result;
    }

    /**
     * Generates a clusterer. Has to initialize all fields of the clusterer that are
     * not being set via options.
     * 
     * @param data set of instances serving as training data
     * @throws Exception if the clusterer has not been generated successfully
     */
    @Override
    public void buildClusterer(Instances data) throws Exception {

        // can clusterer handle the data?
        getCapabilities().testWithFail(data);

        // long start = System.currentTimeMillis();

        m_ReplaceMissingFilter = new ReplaceMissingValues();
        m_ReplaceMissingFilter.setInputFormat(data);
        m_instances = Filter.useFilter(data, m_ReplaceMissingFilter);

        initMinMax(m_instances);

        m_ClusterCentroids = new Instances(m_instances, m_NumClusters);

        int n = m_instances.numInstances();
        Random r = new Random(getSeed());
        boolean[] selected = new boolean[n];
        double[] minDistance = new double[n];

        for (int i = 0; i < n; i++) {
            minDistance[i] = Double.MAX_VALUE;
        }

        int firstI = r.nextInt(n);
        m_ClusterCentroids.add(m_instances.instance(firstI));
        selected[firstI] = true;

        updateMinDistance(minDistance, selected, m_instances, m_instances.instance(firstI));

        if (m_NumClusters > n) {
            m_NumClusters = n;
        }

        for (int i = 1; i < m_NumClusters; i++) {
            int nextI = farthestAway(minDistance, selected);
            m_ClusterCentroids.add(m_instances.instance(nextI));
            selected[nextI] = true;
            updateMinDistance(minDistance, selected, m_instances, m_instances.instance(nextI));
        }

        m_instances = new Instances(m_instances, 0);
        // long end = System.currentTimeMillis();
        // System.out.println("Clustering Time = " + (end-start));
    }

    protected void updateMinDistance(double[] minDistance, boolean[] selected, Instances data, Instance center) {
        for (int i = 0; i < selected.length; i++) {
            if (!selected[i]) {
                double d = distance(center, data.instance(i));
                if (d < minDistance[i]) {
                    minDistance[i] = d;
                }
            }
        }
    }

    protected int farthestAway(double[] minDistance, boolean[] selected) {
        double maxDistance = -1.0;
        int maxI = -1;
        for (int i = 0; i < selected.length; i++) {
            if (!selected[i]) {
                if (maxDistance < minDistance[i]) {
                    maxDistance = minDistance[i];
                    maxI = i;
                }
            }
        }
        return maxI;
    }

    protected void initMinMax(Instances data) {
        m_Min = new double[data.numAttributes()];
        m_Max = new double[data.numAttributes()];
        for (int i = 0; i < data.numAttributes(); i++) {
            m_Min[i] = m_Max[i] = Double.NaN;
        }

        for (int i = 0; i < data.numInstances(); i++) {
            updateMinMax(data.instance(i));
        }
    }

    /**
     * Updates the minimum and maximum values for all the attributes based on a new
     * instance.
     * 
     * @param instance the new instance
     */
    private void updateMinMax(Instance instance) {

        for (int j = 0; j < instance.numAttributes(); j++) {
            if (Double.isNaN(m_Min[j])) {
                m_Min[j] = instance.value(j);
                m_Max[j] = instance.value(j);
            } else {
                if (instance.value(j) < m_Min[j]) {
                    m_Min[j] = instance.value(j);
                } else {
                    if (instance.value(j) > m_Max[j]) {
                        m_Max[j] = instance.value(j);
                    }
                }
            }
        }
    }

    /**
     * clusters an instance that has been through the filters
     * 
     * @param instance the instance to assign a cluster to
     * @return a cluster number
     */
    protected int clusterProcessedInstance(Instance instance) {
        double minDist = Double.MAX_VALUE;
        int bestCluster = 0;
        for (int i = 0; i < m_NumClusters; i++) {
            double dist = distance(instance, m_ClusterCentroids.instance(i));
            if (dist < minDist) {
                minDist = dist;
                bestCluster = i;
            }
        }
        return bestCluster;
    }

    /**
     * Classifies a given instance.
     * 
     * @param instance the instance to be assigned to a cluster
     * @return the number of the assigned cluster as an integer if the class is
     *         enumerated, otherwise the predicted value
     * @throws Exception if instance could not be classified successfully
     */
    @Override
    public int clusterInstance(Instance instance) throws Exception {
        m_ReplaceMissingFilter.input(instance);
        m_ReplaceMissingFilter.batchFinished();
        Instance inst = m_ReplaceMissingFilter.output();

        return clusterProcessedInstance(inst);
    }

    /**
     * Calculates the distance between two instances
     * 
     * @param first  the first instance
     * @param second the second instance
     * @return the distance between the two given instances, between 0 and 1
     */
    protected double distance(Instance first, Instance second) {

        double distance = 0;
        int firstI, secondI;

        for (int p1 = 0, p2 = 0; p1 < first.numValues() || p2 < second.numValues();) {
            if (p1 >= first.numValues()) {
                firstI = m_instances.numAttributes();
            } else {
                firstI = first.index(p1);
            }
            if (p2 >= second.numValues()) {
                secondI = m_instances.numAttributes();
            } else {
                secondI = second.index(p2);
            }
            if (firstI == m_instances.classIndex()) {
                p1++;
                continue;
            }
            if (secondI == m_instances.classIndex()) {
                p2++;
                continue;
            }
            double diff;
            if (firstI == secondI) {
                diff = difference(firstI, first.valueSparse(p1), second.valueSparse(p2));
                p1++;
                p2++;
            } else if (firstI > secondI) {
                diff = difference(secondI, 0, second.valueSparse(p2));
                p2++;
            } else {
                diff = difference(firstI, first.valueSparse(p1), 0);
                p1++;
            }
            distance += diff * diff;
        }

        return Math.sqrt(distance / m_instances.numAttributes());
    }

    /**
     * Computes the difference between two given attribute values.
     */
    protected double difference(int index, double val1, double val2) {

        switch (m_instances.attribute(index).type()) {
        case Attribute.NOMINAL:

            // If attribute is nominal
            if (Utils.isMissingValue(val1) || Utils.isMissingValue(val2) || ((int) val1 != (int) val2)) {
                return 1;
            } else {
                return 0;
            }
        case Attribute.NUMERIC:

            // If attribute is numeric
            if (Utils.isMissingValue(val1) || Utils.isMissingValue(val2)) {
                if (Utils.isMissingValue(val1) && Utils.isMissingValue(val2)) {
                    return 1;
                } else {
                    double diff;
                    if (Utils.isMissingValue(val2)) {
                        diff = norm(val1, index);
                    } else {
                        diff = norm(val2, index);
                    }
                    if (diff < 0.5) {
                        diff = 1.0 - diff;
                    }
                    return diff;
                }
            } else {
                return norm(val1, index) - norm(val2, index);
            }
        default:
            return 0;
        }
    }

    /**
     * Normalizes a given value of a numeric attribute.
     * 
     * @param x the value to be normalized
     * @param i the attribute's index
     * @return the normalized value
     */
    protected double norm(double x, int i) {

        if (Double.isNaN(m_Min[i]) || Utils.eq(m_Max[i], m_Min[i])) {
            return 0;
        } else {
            return (x - m_Min[i]) / (m_Max[i] - m_Min[i]);
        }
    }

    /**
     * Returns the number of clusters.
     * 
     * @return the number of clusters generated for a training dataset.
     * @throws Exception if number of clusters could not be returned successfully
     */
    @Override
    public int numberOfClusters() throws Exception {
        return m_NumClusters;
    }

    /**
     * Get the centroids found by FarthestFirst
     * 
     * @return the centroids found by FarthestFirst
     */
    public Instances getClusterCentroids() {
        return m_ClusterCentroids;
    }

    /**
     * Returns an enumeration describing the available options.
     * 
     * @return an enumeration of all the available options.
     */
    @Override
    public Enumeration<Option> listOptions() {
        Vector<Option> result = new Vector<Option>();

        result.addElement(new Option("\tnumber of clusters. (default = 2).", "N", 1, "-N <num>"));

        result.addAll(Collections.list(super.listOptions()));

        return result.elements();
    }

    /**
     * Returns the tip text for this property
     * 
     * @return tip text for this property suitable for displaying in the
     *         explorer/experimenter gui
     */
    public String numClustersTipText() {
        return "set number of clusters";
    }

    /**
     * set the number of clusters to generate
     * 
     * @param n the number of clusters to generate
     * @throws Exception if number of clusters is negative
     */
    public void setNumClusters(int n) throws Exception {
        if (n < 0) {
            throw new Exception("Number of clusters must be > 0");
        }
        m_NumClusters = n;
    }

    /**
     * gets the number of clusters to generate
     * 
     * @return the number of clusters to generate
     */
    public int getNumClusters() {
        return m_NumClusters;
    }

    /**
     * Parses a given list of options.
     * <p/>
     * 
     * <!-- options-start --> Valid options are:
     * <p/>
     * 
     * <pre>
     *  -N &lt;num&gt;
     *  number of clusters. (default = 2).
     * </pre>
     * 
     * <pre>
     *  -S &lt;num&gt;
     *  Random number seed.
     *  (default 1)
     * </pre>
     * 
     * <!-- options-end -->
     * 
     * @param options the list of options as an array of strings
     * @throws Exception if an option is not supported
     */
    @Override
    public void setOptions(String[] options) throws Exception {

        String optionString = Utils.getOption('N', options);

        if (optionString.length() != 0) {
            setNumClusters(Integer.parseInt(optionString));
        }

        super.setOptions(options);

        Utils.checkForRemainingOptions(options);
    }

    /**
     * Gets the current settings of FarthestFirst
     * 
     * @return an array of strings suitable for passing to setOptions()
     */
    @Override
    public String[] getOptions() {

        Vector<String> result = new Vector<String>();

        result.add("-N");
        result.add("" + getNumClusters());

        Collections.addAll(result, super.getOptions());

        return result.toArray(new String[result.size()]);
    }

    /**
     * return a string describing this clusterer
     * 
     * @return a description of the clusterer as a string
     */
    @Override
    public String toString() {
        StringBuffer temp = new StringBuffer();

        temp.append("\nFarthestFirst\n==============\n");

        temp.append("\nCluster centroids:\n");
        for (int i = 0; i < m_NumClusters; i++) {
            temp.append("\nCluster " + i + "\n\t");
            for (int j = 0; j < m_ClusterCentroids.numAttributes(); j++) {
                if (m_ClusterCentroids.attribute(j).isNominal()) {
                    temp.append(" " + m_ClusterCentroids.attribute(j).value((int) m_ClusterCentroids.instance(i).value(j)));
                } else {
                    temp.append(" " + m_ClusterCentroids.instance(i).value(j));
                }
            }
        }
        temp.append("\n\n");
        return temp.toString();
    }

    /**
     * Main method for testing this class.
     * 
     * @param argv should contain the following arguments:
     *             <p>
     *             -t training file [-N number of clusters]
     */
    public static void main(String[] argv) {
        runClusterer(new FarthestFirst(), argv);
    }
}
